One problem associated with perception of information is overload. Generally, this problem is addressed by distinguishing between the information perceived from a given scene that is considered important and that which is not important. In techniques for processing visual information, such as in a digital image, a determination of the important information is made based on an analysis of the portions of visual information that are considered “salient”. In general, visual “saliency” at a given location (e.g., a pixel of an image) refers to how different the given location is from its surrounding in terms of color, orientation, motion, depth, and so forth.
For a scene, such as that depicted in an image, a saliency map may be generated that represents a visual saliency of the scene at each location in the image, e.g., the saliency of the scene at each pixel of the image relative to the scene at the other pixels. However, conventional techniques for computing saliency maps may not retain high frequency details of a scene. In other words, conventionally computed saliency maps may be blurry. In addition, conventional techniques may be computationally inefficient. Consequently, the suitability of conventionally-computed saliency maps may be limited for some purposes.